论文标题
使用特定主题的医学数据进行解剖学预测
Anatomical Predictions using Subject-Specific Medical Data
论文作者
论文摘要
随着时间的流逝，大脑解剖结构的变化可以为治疗设计或科学分析提供重要的见解。我们提出了一种预测个人大脑MRI如何随着时间而变化的方法。我们使用使用卷积神经网络使用函数预测的差异变形场对更改进行建模。鉴于预测的变形场，可以扭曲基线扫描，以预测将来的脑部扫描。我们使用ADNI队列演示了该方法，并分析了模型变体和所提供的主题特定信息如何影响性能。我们表明该模型提供了良好的预测，并且外部临床数据可以改善预测。
Changes over time in brain anatomy can provide important insight for treatment design or scientific analyses. We present a method that predicts how a brain MRI for an individual will change over time. We model changes using a diffeomorphic deformation field that we predict using function using convolutional neural networks. Given a predicted deformation field, a baseline scan can be warped to give a prediction of the brain scan at a future time. We demonstrate the method using the ADNI cohort, and analyze how performance is affected by model variants and the subject-specific information provided. We show that the model provides good predictions and that external clinical data can improve predictions.
